Basics of Data Analysis

26/5/2021

Session Outcome

Upon successful completion of this session, the participants will be able to:

  1. learn the data analysis process cycle

  2. develop a basic knowledge on various type of data analysis and its relevance

What is Data Analysis?

Data analysis is the process of exploring and analyzing datasets to find hidden patterns.

In Business applications the data size is large and demand more high level specific predictions to boost business. Tools for this specific tasks come under Data Analytics

Why data Analytics?

Companies ideally need to use all of their generated data to derive value out of it and make impactful business decisions. Data analytics is used to drive this purpose.

Is Python Necessary in the Data Science Field?

Is Python Better than R for Data Science?

How is Python Used for Data Science?

  1. Using Python and SQL, you write a query to pull the data you need from your company database.

  2. Using Python and the pandas library, you clean and sort the data into a dataframe (table) that’s ready for analysis.

  3. Using Python, with pandas and matplotlib libraries, you begin analyzing, exploring, and visualizing the data.

  4. After learning more about the data through your exploration, you use Python with the scikit-learn library to build a predictive model that forecasts future outcomes for your company based on the data you pulled.

  5. You arrange your final analysis and your model results into an appropriate format for communicating with your co-workers.

Steps in Data analysis

Data Processing- the most time consuming stage

What Types of Data Analysis are There?

  1. Diagnostic Analysis: Answers the question, “Why did this happen?” – Pattern identification

  2. Predictive Analysis: Answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events.

  3. Prescriptive Analysis: Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.

  4. Statistical Analysis: Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:

  1. Descriptive: Explain “What the data says”

  2. Inferential: Generalizes the observations from descriptive statistic by testing of hypothesis on samples

  1. Text Analysis: Also called “data mining”, text analysis uses databases and data mining tools to discover patterns residing in large datasets.

Questions?

End

thanks